Modeling and Prediction of Cryptocurrency Prices Using Machine Learning Techniques

With the introduction of Bitcoin in the year 2008 as the first practical decentralized cryptocurrency , the interest in cryptocurrencies and their underlying technology , Blockchain , has skyrocketed. Their promise of security , anonymity , and lack of a central controlling authority make them ideal for users who value their privacy. Academic research on machine learning , Blockchain technology , and their intersection have increased significantly in recent years. Specifically , one of the interest areas for researchers is the possibility of predicting the future prices of these cryptocurrencies using supervised machine learning techniques. In this thesis , we investigate their ability to make one day ahead price prediction of several popular cryptocurrencies using five widely used time-series prediction models. These models are designed by optimizing model parameters , such as activation functions , before settling on the final models presented in this thesis. Finally , we report the performance of each time-series prediction model measured by its mean squared error and accuracy in price movement direction prediction